With the development of smart grid, household load forecasting played an important role in power system operations. However, the household load forecasting is often inefficient due to its high volatility and uncertainty. Consequently, a multi-objective LSTM ensemble model based on the DBN combination strategy, is proposed in this paper. This method first builds a deep learning framework based on the LSTM network in order to generate several sub-models. With the diversity and accuracy of the sub-models as the objective functions, the improved MOEA/D algorithm is then used to optimize the parameters, in order to enhance the overall performance of the sub-models and ensure their differences. Finally, a DBN-based combination strategy is used to combine the single forecasts in order to form the ensemble forecast, and reduce the adverse effects of model uncertainty and data noise on the prediction accuracy. The experimental results show that the proposed method has several advantages in prediction accuracy and generalization capacity, compared with several current intelligent prediction methods.

随着智能电网的发展，家庭负荷​​预测在电力系统运行中发挥了重要作用。然而，由于其高波动性和不确定性，家庭负荷​​预测往往效率低下。因此，本文提出了一种基于 DBN 组合策略的多目标 LSTM 集成模型。该方法首先构建基于 LSTM 网络的深度学习框架，以生成多个子模型。以子模型的多样性和准确性为目标函数，然后使用改进的MOEA/D算法对参数进行优化，以提高子模型的整体性能并保证它们之间的差异。最后，使用基于 DBN 的组合策略将单个预测组合起来，形成集成预测，减少模型不确定性和数据噪声对预测精度的不利影响。实验结果表明，与当前几种智能预测方法相比，该方法在预测精度和泛化能力方面具有多项优势。